JBudgeME

from fasthtml import FastHTML
from fasthtml.common import *

app = FastHTML(hdrs=(picolink,))

def create_card(number):
    return Div(Small(f"Item {number}"))

@app.get("/")
def home():

thomwolf

""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory
    git clone https://github.com/myshell-ai/OpenVoice
    cd OpenVoice
    git clone https://huggingface.co/myshell-ai/OpenVoice
    cp -r OpenVoice/* .
    pip install whisper pynput pyaudio
"""

from openai import OpenAI
import time

ivanfioravanti

import json
import os
import ollama


def query_ollama(prompt, model='openhermes:7b-mistral-v2.5-q6_K', context=''):
    response = ollama.generate(
        model=model,
        prompt=context + prompt)
    return response['response'].strip()

karpathy

"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:

$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

ines

pip install streamlit
pip install spacy
python -m spacy download en_core_web_sm
python -m spacy download en_core_web_md
python -m spacy download de_core_news_sm

aneesh-joshi

import tensorflow as tf
import numpy as np

corpus_raw = 'He is the king . The king is royal . She is the royal  queen '

# convert to lower case
corpus_raw = corpus_raw.lower()

words = []
for word in corpus_raw.split():

mommi84

Awesome Knowledge Graph Embedding Approaches

This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.

Libraries

• 🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
• Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding

shagunsodhani

Question Answering with Subgraph Embeddings

Introduction

• Open-domain Question Answering (Open QA) - efficiently querying large-scale knowledge base(KB) using natural language.
• Two main approaches:
  • Information Retrieval
    • Transform question (in natural language) into a valid query(in terms of KB) to get a broad set of candidate answers.
    • Perform fine-grained detection on candidate answers.
• Semantic Parsing

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)